Instructions to use skratos115/qwen2-7b-OpenDevin-f16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use skratos115/qwen2-7b-OpenDevin-f16 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="skratos115/qwen2-7b-OpenDevin-f16",
	filename="qwen2.7b.OD..f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use skratos115/qwen2-7b-OpenDevin-f16 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf skratos115/qwen2-7b-OpenDevin-f16:F16
# Run inference directly in the terminal:
llama-cli -hf skratos115/qwen2-7b-OpenDevin-f16:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf skratos115/qwen2-7b-OpenDevin-f16:F16
# Run inference directly in the terminal:
llama-cli -hf skratos115/qwen2-7b-OpenDevin-f16:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf skratos115/qwen2-7b-OpenDevin-f16:F16
# Run inference directly in the terminal:
./llama-cli -hf skratos115/qwen2-7b-OpenDevin-f16:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf skratos115/qwen2-7b-OpenDevin-f16:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf skratos115/qwen2-7b-OpenDevin-f16:F16

Use Docker

docker model run hf.co/skratos115/qwen2-7b-OpenDevin-f16:F16

LM Studio
Jan

vLLM

How to use skratos115/qwen2-7b-OpenDevin-f16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "skratos115/qwen2-7b-OpenDevin-f16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "skratos115/qwen2-7b-OpenDevin-f16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/skratos115/qwen2-7b-OpenDevin-f16:F16

Ollama
How to use skratos115/qwen2-7b-OpenDevin-f16 with Ollama:
```
ollama run hf.co/skratos115/qwen2-7b-OpenDevin-f16:F16
```

Unsloth Studio

How to use skratos115/qwen2-7b-OpenDevin-f16 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for skratos115/qwen2-7b-OpenDevin-f16 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for skratos115/qwen2-7b-OpenDevin-f16 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for skratos115/qwen2-7b-OpenDevin-f16 to start chatting

Docker Model Runner
How to use skratos115/qwen2-7b-OpenDevin-f16 with Docker Model Runner:
```
docker model run hf.co/skratos115/qwen2-7b-OpenDevin-f16:F16
```

Lemonade

How to use skratos115/qwen2-7b-OpenDevin-f16 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull skratos115/qwen2-7b-OpenDevin-f16:F16

Run and chat with the model

lemonade run user.qwen2-7b-OpenDevin-f16-F16

List all available models

lemonade list

Qwen2.7b.OpenDevin

brought to you by skratos115 (HF) / Kingatlas115 (GH) in colaboration with the official Opendevin Team ~xingyaoww

Qwen2-7B-Instruct with OpenDevin Tool Calling

Overview

This project involves the fine-tuning of the Qwen2-7B-Instruct model using the opendevin-code-act dataset with the help of Unsloth. The primary goal is to develop a more powerful LLM capable of effectively using the CodeAct framework for tool calling. This is still in early development and should not be used in production. We are working on building a bigger dataset for tool paths/ trajectories and could you all the help we can by using the feedback integration to help us build better trajectories and release to the public via MIT license for OSS model training. read more here:https://x.com/gneubig/status/1802740786242420896 and http://www.linkedin.com/feed/update/urn:li:activity:7208507606728929280/

Model Details

Model Name: Qwen2-7B-Instruct
Dataset: opendevin-code-act
Training Platform: Unsloth

provided full merged files or Quantized f16, q4_k_m, Q5_k_m, and Q8_0 gguf files. I used the qwen2.7b.OD.q4_k_m.gguf for my testing and got it to write me a simple script. more testing to come.

Running the Model

You can run this model using vLLM or ollama. The following instructions are for using ollama.

Prerequisites

Docker
Hugging Face transformers library (version >= 4.37.0 is recommended)

q-4k ollama run skratos115/qwen2-7b-opendevin-q4_k_m

f16 ollama run skratos115/qwen2-7b-opendevin-f16

Running with Ollama

Install Docker: Ensure you have Docker installed on your machine.
Pull the Latest Hugging Face Transformers:

pip install transformers>=4.37.0
Set Up Your Workspace:

WORKSPACE_BASE=$(pwd)/workspace
Run the Docker Command: docker run -it
--pull=always
-e SANDBOX_USER_ID=$(id -u)
-e PERSIST_SANDBOX="true"
-e LLM_API_KEY="ollama"
-e LLM_BASE_URL="http://[yourIPhere or 0.0.0.0]:11434"
-e SSH_PASSWORD="make something up here"
-e WORKSPACE_MOUNT_PATH=$WORKSPACE_BASE
-v $WORKSPACE_BASE:/opt/workspace_base
-v /var/run/docker.sock:/var/run/docker.sock
-p 3000:3000
--add-host host.docker.internal:host-gateway
--name opendevin-app-$(date +%Y%m%d%H%M%S)
ghcr.io/opendevin/opendevin:main

Replace [yourIPhere or 0.0.0.0] with your actual IP address or use 0.0.0.0 for localhost.

Early Development

This project is in its early stages, and we are continuously working to improve the model and its capabilities. Contributions and feedback are welcome.

Support my work

Right now all of my work has been funded personally, if you like my work and can help support growth in the AI community consider joining or donating to my Patreon. Patreon Link

License

This project is licensed under the MIT License.

Downloads last month: 17

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

16-bit

skratos115
/

qwen2-7b-OpenDevin-f16